A multiple Beam Antenna (MBA) is a type of antenna system employed in the satellite applications and under normal operating mode has a field of view (FOV) that covers a section of the Earth's surface termed the coverage area. The MBA contains several feeds that illuminate a lens or a reflector system to produce multiple beams. The adaptive MBA is designed to combat interference sources including intentional jammers located anywhere within the desired area covered by the quiescent pattern of the antenna. This is accomplished by relatively high resolution afforded by a multibeam antenna with a relatively large aperture size and a significant number of beams providing the desired degrees of freedom in the adaptive nulling process. The outputs of the multiple beams are linearly combined in the MBA beamformer in an adaptive manner so as to introduce nulls in the direction of interference sources while minimizing the inevitable antenna gain reduction in any other direction within the quiescent beam width. To achieve the desired objectives effectively, the adaptive algorithm must have sufficiently rapid convergence rate to adapt in a dynamic scenario, such as the presence of blinking jammers, and have reasonable computational requirements in terms of the actual number of arithmetic operations per update of the algorithm.
The adaptive algorithms of the prior art are based on the constrained optimization of the array gain related to the signal to noise power ratio for a signal source in the direction of the peak of the antenna gain as specified by the steering vector. The steering vector or the quiescent weight vector actually produces reasonable directive gain to users in the specified coverage area in the absence of jammers—essentially a property of the MBA antenna.
However, this is not the case with the adapted pattern obtained with the adaptive algorithm in the presence of jammers wherein there is considerable reduction in the antenna gain in the coverage area with the reduction being highly non uniform over the area of coverage. One of the adaptive algorithms termed SMI (Sample Matrix Inversion) minimizes the residual interference power at the MBA beamformer output. However, the SMI algorithm does not optimize the area covered by the adapted beam. In terms of implementation, the SMI algorithm requires N2 complex multiplications and additions and an N×N matrix inversion for each algorithm update wherein N is the number of beams in the MBA. Furthermore, in situations involving multiple jammers of varying power levels, the estimated correlation matrix may become ill conditioned and high precision arithmetic may be required to avoid numerical instability. A computationally simpler algorithm termed correlation feedback (CF) requires only order N computations. However, for the case of high condition number of the relevant correlation matrix involved, the convergence may be very slow and may not be acceptable in some dynamic scenario.
Quantized state (QS) algorithms taught by Kumar have convergence rates similar to that of SMI algorithm and possibly orders of magnitude faster than the CF algorithm. The QS algorithm require much smaller computational requirements compared to the SMI algorithm and are also numerically robust with matrix condition number that is order of the square root of the condition number of the correlation matrix involved in the SMI algorithm. The quantizes state algorithms are also capable of providing better gain distribution in the coverage area than the SMI algorithm especially in areas in the vicinity of the interference sources.
The adaptive algorithms of the previous literature exhibit spurious nulls in the adapted MBA beam pattern, in that the antenna gain pattern has nulls in the directions other than the direction of the interfering sources. The number of the spurious nulls, their locations and depth is highly variable and depends upon the algorithm used, the number of interfering sources, their directions and power levels, etc., and is otherwise unpredictable. Due to these spurious nulls, the coverage area of the MBA antenna has undesirable holes around these nulls in addition to those around the interfering sources.
Thus it is desirable to have architectures that eliminate the presence of spurious nulls in the area of coverage, provide superior antenna gain distribution in the coverage area compared to the architectures of the prior art, and result in a higher signal to interference plus noise ratio for the users located in the coverage area. It may be also desirable to be able to have architectures for finding the directions of the interference sources including intentional jammers for RF surveillance purposes. The adaptive multibeam architectures of this invention possess these and various other benefits.